I'm in the same boat as you, having only read non-fiction the past few years. It appears quite ironic that the entertainment occurring in the real life events is surpassing the entertainment I hear one usually derives from fiction.
That's a good point. I've added to the post. It's simple enough I'd expect anyone who wants to do it can follow the docs from CADO-NFS, which are very good, but I can write it down explicitly.
Install CADO-NFS per upstream directions.
Get the number you want to factor. I just did this in a python repl, something like:
from cryptography import x509
f = open("gold-server.pem", "rb").read()
print(x509.load_pem_x509_certificate(f).public_key().public_numbers().n)
That gets you the `n` to factor - The big number below.
Then pass it to CADO-NFS. The full invocation for the server root was:
Why? I feel it kinda implies what the approach is. Could definitely be worse, strongly prefer this over just giving the thing a random-ass name like Laguna.
> Hydra (sometimes stylized as HYDRA) is a fictional terrorist organization appearing in American comic books published by Marvel Comics.[…] Hydra is taken over and turned into a neo-fascist international crime syndicate by Baron Wolfgang von Strucker.
With all due respect, but I'm not letting some newfangled comic taint/monopolize a perfectly usable name that's been used to describe many-headed-thing (water snake) for over two millennia.
I don't think I could ever go back to running an e-mail server again, given the continually evolving battle with convincing the major e-mail providers that your IP is legit (I'm looking at you AT&T!).
I wonder if you put job classifications along a spreadsheet's leftmost column (e.g. search in file, search in directory), and data types along a spreadsheet's topmost row (e.g. JSON file, macOS-based filesystem), would you end up finding gaps in the intersection of the columns and rows make these new tools you've created less surprising? For example, to "search a file" on a "macOS-based filesystem", you would naturally gravitate towards `grep`. Or to search the contents of a JSON file, you would maybe use `grep`, or perhaps `jq` and a well crafted query. Well, as you expand out the job classifications and data types (or some other abstraction), you start to realize where we don't have tools today. And then Claude can go and create those, perhaps even proposing a faster alternative!
I logged in just to share the entire comment for you:
>The big milestone is when computers can read and understand information like humans do. There is a lot of work going on in this field - Google, Microsoft, Facebook, academia,... Right now computers don't know how to represent knowledge so they can't read a text book and pass a test.
>Another whole area is vaccines. We need a vaccine for HIV, Malaria and TB and I hope we have them in the next 10-15 years.
Maybe I'm being super reductive here, but operating small models at the core of your business kind of moves the needle from making external API calls (against frontier models) to running internal API calls (against your locally-run models). It seems like if we want local models to take off, it will need to become easier to run local models for cheap. I'm thinking like reducing the barrier of entry for running "local models" in the cloud providers like DigitalOcean, AWS, etc.
I only run local models and I don't give them access to much externally. I don't do anything serious with it, but it comes in handy and I know that they can do so much more. I'm on a meager RTX 3060 12GB and a GTX 1660 Ti with 6GB for some extra vram space. When I first started playing with local models, I was really impressed with what I was able to achieve locally.
That's great, but the thing that worries me is that many companies have billions invested in the AI bubble. It's around 1.5 trillion last time I looked. It's all circular spending between the companies building out the infrastructure, and the models. None of it is profitable. They will want to recoup that 1.5 trillion from consumers, which means using online-only pay-as-you-go cloud models. They will inevitably see that people using capable local AI are "lost customers" and they will try to kill the ability to locally host AI or somehow enshitify it enough to make paying a subscription more palatable.
I'm not saying I believe that will happen, I'm just worried that it will. Is anyone else worried about that as well?
I am very excited that more makers will come up with fast memory for consumers rather than enterprise. Companies can only pre order so much RAM.
At some point there will be a surplus of fast memory and even in a crash the current generation of SLMs are bounced to be plenty to build a lot of intelligence at home.
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